Laser & Optoelectronics Progress, Volume. 57, Issue 13, 130104(2020)
Digital Elevation Model Generation in LiDAR Point Cloud Based on Cloth Simulation Algorithm
Figures & Tables(10)
Fig. 2. Representative terrain of 6 groups. (a) Original point cloud data; (b) corresponding image
Fig. 4. Six groups of ground points and non-ground points extracted by CSF algorithm. (a) Ground; (b) non-ground
Fig. 5. Accuracy evaluation. (a) DEM raster generated by ground points; (b) evaluation curve of fitting accuracy
Table 1. Geomorphic features of sample data
View tableTable 1. Geomorphic features of sample data
Type Data Area /km×km Geomorphic feature Real two kinds of point Ground Non-ground Rural sample 1 0.47×0.45 mixture of vegetation and building 1459772 1490737 Urban sample 2 0.63×0.61 dense buildings, mixed vegetation 612550 480565 Rural sample 3 1.04×0.45 river bank vegetation 247750 55829 Coteau sample 4 1.18×1.18 housing mountain replacement, complex terrain 4888419 2825276 Coteau sample 5 0.58×0.40 slopes are densely vegetated and steep 1867973 2451113 Rural sample 6 0.32×0.30 low house is connected with a gentle slope 570986 762549
Table 2. RI and steep slope treatment in different terrain areas
View tableTable 2. RI and steep slope treatment in different terrain areas
Data Geomorphic feature RI ST Sample 1 gentle slope 2 T Sample 2 flat topography 1 F Sample 3 gentle slope 2 T Sample 4 gradual slope 2 T Sample 5 high steep 3 T Sample 6 gentle slope 2 T
Table 3. Cross table
View tableTable 3. Cross table
Real two kinds of point CSF algorithm Sum Ground Non-ground Ground a b e=a+b Non-ground c d f=c+d Sum g=a+c h=b+d n=a+b+c+d
Table 4. Three error values and Kappa coefficients obtained by different Hcc
View tableTable 4. Three error values and Kappa coefficients obtained by different Hcc
Data GR /m Hcc /m XTypeI /% XTypeII /% XTotal /% XKappa Sample 1 0.5 0.4 6.22 9.76 8.01 0.840 0.5 3.82 10.20 7.04 0.859 0.6 2.50 10.50 6.55 0.869 0.7 1.82 10.70 6.32 0.874 0.8 1.40 10.90 6.23 0.875 0.9 1.20 11.10 6.21 0.876 1.0 1.08 11.22 6.20 0.876 Sample 2 0.4 0.3 0.70 4.20 2.30 0.954 0.4 0.50 5.10 2.50 0.948 0.5 0.40 5.80 2.80 0.943 0.6 0.30 6.50 3.00 0.938 0.7 0.30 7.00 3.20 0.936 0.8 0.20 7.60 3.50 0.928 Sample 3 0.5 0.7 2.09 7.81 3.14 0.896 0.8 1.90 8.00 2.99 0.900 0.9 1.68 8.19 2.87 0.904 1.0 1.50 8.39 2.79 0.906 1.1 1.40 8.60 2.70 0.908 1.2 1.30 8.80 2.70 0.910 1.3 1.19 9.14 2.60 0.910 1.4 1.10 9.45 2.60 0.910 Sample 4 0.5 0.5 1.50 15.90 6.70 0.851 0.6 1.20 16.40 6.80 0.850 0.7 1.00 16.80 6.80 0.848 Sample 5 0.3 0.4 1.67 6.84 4.61 0.907 0.5 0.90 7.19 4.44 0.910 0.6 0.60 7.27 4.41 0.911 0.7 0.40 7.48 4.46 0.910 0.8 0.35 7.71 4.53 0.909 0.9 0.26 7.99 4.65 0.906 1.0 0.19 8.30 4.79 0.903 Sample 6 0.5 0.3 4.10 3.30 3.70 0.925 0.4 2.90 3.80 4.20 0.930 0.5 2.10 4.30 3.30 0.932 0.6 1.50 4.70 3.40 0.932 0.7 1.10 5.20 3.40 0.930
Table 5. Average value of filter error and Kappa coefficient
View tableTable 5. Average value of filter error and Kappa coefficient
Data XTypeI /% XTypeII /% XTotal /% XKappa Sample 1 2.58 10.63 6.65 0.867 Sample 2 0.40 6.03 2.88 0.941 Sample 3 1.79 8.36 2.99 0.900 Sample 4 1.23 16.37 6.77 0.850 Sample 5 0.62 7.54 4.56 0.908 Sample 6 2.34 4.26 3.60 0.930
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Fan Zhang, Huashan Li, Tao Jiang. Digital Elevation Model Generation in LiDAR Point Cloud Based on Cloth Simulation Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(13): 130104
Paper Information
Category: Atmospheric Optics and Oceanic Optics
Received: Oct. 16, 2019
Accepted: Dec. 11, 2019
Published Online: Jul. 9, 2020
The Author Email: Fan Zhang (zf641716907@qq.com;), Tao Jiang (tjiang@126.com)
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